Method and Apparatus for Segmenting Images

ABSTRACT

Method and apparatus for extending regions in two-dimensional (2-D) image space or volumes in three-dimensional (3-D) image space that are generated by a test area-based region growing mechanism. Embodiments of a dilation mechanism may perform post-processing of a region or volume generated by the test area-based region growing mechanism to correct for an edge inset resulting from a radius used to define the test area. The dilation mechanism may perform a morphological dilate to expand the region or volume to the proper edge of the desired object in the image data within the tolerance range of the threshold, and thus corrects for the inset error introduced by the test area radius used by the region growing mechanism. The dilation mechanism may be limited to extending the region or volume to the radius distance from the edge of the original region or volume generated by the region growing mechanism.

BACKGROUND

1. Field of the Invention

This invention relates to digital image processing, and morespecifically to digital image segmentation methods.

2. Description of the Related Art

The capturing and processing of digital images has applications in manyfields, including but not limited to applications in the medical,astronomy, and security fields. Digital images may be captured using avariety of mechanisms. Conventionally, images may be captured in theelectromagnetic spectrum, including visible light images, infraredimages, X-Ray images, radiotelescope images, radar images such asDoppler Radar images, and so on. Common digital image capturingmechanisms for images in the electromagnetic spectrum include, but arenot limited to, digitizing cameras that include EM spectrum-sensitivesensors that directly digitize images in the electromagnetic spectrumand film scanners that digitize conventionally photographed or otherwisecaptured images from film or from photographs. Note that digital imagesmay be captured as grayscale (“black and white”) images or color images,and that color images may be converted to grayscale images and viceversa. Also note that digital images may be captured for viewing objectsand events from the subatomic level in particle accelerators, throughthe microscopic level, and on up to the level of clusters of deep-spacegalaxies as captured by the Hubble Space Telescope.

Digital images may be generated by other methods than directly capturingimages in the electromagnetic spectrum. For example, in the medicalfield, non-invasive mechanisms have been developed for viewing theinternal organs and other internal structures other than theconventional X-Ray. Examples include Magnetic Resonance Imaging (MRI),computed tomography (CT), positron emission tomography (PET), andultrasound systems. In these systems, data is captured using someindirect mechanism and converted into “conventional” viewable grayscaleor color images. Note that at least some of these mechanisms have foundapplications in other fields than the medical field, for example, invarious engineering fields, for example in examining the structuralintegrity of metals such as structural steel or airplane parts and, inpaleontology, in non-invasively viewing the internal structure of afossil.

Some image capturing mechanisms, such as MRI, CT, and PET systems, maybe able to capture digital image “slices” of an object such as a humanbody. For example, an MRI system may be used to generate a set of imagesrepresenting “slices” taken through an portion of the human bodycontaining a particular internal organ or organs or other structure—eventhe entire body may be so imaged. This set of images may be viewed as athree-dimensional (3-D) rendering of the object. Each individual imagemay be viewed as a “conventional” two-dimensional (2-D) image, but theset of captured images contains data that represents three-dimensionalinformation, and that may be rendered using various rendering techniquesinto 3-D representations of a particular object or structure.

FIG. 1 illustrates a two-dimensional (2-D) digital image. A pixel(“picture element”) is the basic element or unit of graphic informationin a 2-D digital image. A pixel defines a point in two-dimensional spacewith an x and y coordinate. FIG. 2 illustrates pixels in 2-D imagespace. Each cube in FIG. 2 represents one element of graphicinformation, or pixel. Each pixel may be specified by a coordinate onthe x and y axes as (x,y). In 2-D image space, a pixel (not on the edgeof the image) may be considered as having eight adjacent pixels, ifdiagonally-adjacent pixels are considered as connected. For example,pixel (1,1) in FIG. 2 has eight-connected pixels. If diagonally-adjacentpixels are not considered as connected, a non-edge pixel would have onlyfour connected pixels.

In 2-D image space, a region is a connected set of pixels; that is, aset of pixels in which all the pixels are adjacent and connected, and inwhich the pixels are typically associated according to some othercriterion or criteria. In 2-D image space, four-connectivity is whenonly laterally-adjacent pixels are considered connected;eight-connectivity is when laterally-adjacent and diagonally-adjacentpixels are considered connected.

FIG. 3 illustrates a three-dimensional (3-D) digital image set. A 3-Ddigital image is essentially a stack or set of 2-D images, with eachimage representing a “slice” of an object being digitized. A voxel(“volume element”) is an element or unit of graphic information thatdefines a point in the three-dimensional space of a 3-D digital image. Apixel defines a point in two-dimensional space with an x and ycoordinate; a third coordinate (z) is used to define voxels in 3-Dspace. A voxel may be specified by its coordinates on the three axes,for example as (x,y,z) or as (z,x,y). FIG. 4A illustrates voxels in 3-Dimage space. Each cube in FIG. 4A represents one element of graphicinformation, or voxel. Each voxel may be specified by a coordinate onthe x, y and z axes as (x,y,z) or (z,x,y). In 3-D image space, a voxel(not on the edge of the image) may be considered as having 26 adjacentvoxels, if diagonally-adjacent voxels are considered as connected. Forexample, voxel (1,1,1) in FIG. 4A has 26 connected voxels. FIG. 4B showsadjacent voxels to voxel (1,1,1) if diagonally-adjacent voxels are notconsidered as connected. In FIG. 4B, voxel (1,1,1) has six connectedvoxels.

In 3-D image space, a volume is a connected set of voxels; that is, aset of voxels in which all the voxels are adjacent and connected, and inwhich the voxels are typically associated according to some othercriterion or criteria. In 3-D image space, six-connectivity is when onlylaterally-located voxels are considered connected; 26-connectivity iswhen laterally-adjacent and diagonally-adjacent voxels are consideredconnected.

For simplicity, the term “element” may be used herein to refer to bothpixels and voxels. In a digital image, each element (pixel or voxel) maybe defined in terms of at least its position and graphic information(e.g., color, and density). The position specifies the location of theelement in the 2-D or 3-D image space as coordinates. Color and densityare components of the graphic information at the corresponding position.In a color image, the specific color that an element (pixel or voxel)describes is a blend of three components of the color spectrum (e.g.,RGB). Note that some graphic information (e.g., color) may not beactually “captured” but instead may be added to an image. For example,an image captured in grayscale may have “pseudocolors” added for variousreasons. An example is the various shades of blue, yellow, green and redadded to Doppler Radar-captured images to represent various amounts ofrainfall over a geographic region.

Image Segmentation

Image segmentation refers to a specific area within digital imageprocessing. Image segmentation is one area within the broader scope ofanalyzing the content of digital images. Image segmentation may also beviewed as the process of locating and isolating objects within a digitalimage. Various image segmentation techniques have been developed thatmay be used in various image processing task such as in locating one ormore objects of interest, in separating or segmenting two or moreadjacent objects, in finding boundaries of objects, in extractingobjects of interest from images, in finding objects or structures withinobjects, and so on.

Image segmentation techniques generally fall into three differentcategories that represent three different approaches to the imagesegmentation problem. In boundary locating techniques, boundariesbetween regions or volumes are located. In edge detection techniques,edge pixels or voxels of objects or structures within an image areidentified and then linked to form boundaries. In region growingtechniques, each element or a portion of the elements within an imagemay be assigned to a particular object, region (for 2-D images), orvolume (for 3-D images). Note that, in 3-D image space, “volume growing”is analogous to region growing, although as each image in a set of imageconstituting a 3-D data set is essentially a 2-D image, region growingtechniques may be applied to the individual images or ‘slices’ in the3-D data set. Further note that region growing techniques developed for2-D image space can generally be adapted to work in 3-D image space togrow volumes. To avoid confusion herein, the techniques will simply bereferred to as “region growing” techniques, although it is important tonote that the techniques may be adapted to grow regions in 2-D space orvolumes in 3-D space.

As an example of an application of image segmentation techniques, 2-Dand 3-D medical images may be segmented for a variety of reasons. Forexample, in order to measure the volume of a growth or tumor, or todisplay the coronal tree as a 3D model, sections of 3-D image datacaptured by MRI, CT Scan, or some other mechanism have to be selectedand separated from the entire dataset.

Image segmentation of medical images may be been done manually, andconventionally manual methods have been commonly used. The manual methodrequires the user to “lasso” the desired pixels in a 2-D image, orvoxels in each image in a 3-D data set of two or more captured images or“slices”. Manual image segmentation can be a difficult andtime-consuming task, especially in the multi-slice case.

Several automated region growing image segmentation techniques thatattempt to grow a region (or volume) from a seed location (indicating aparticular pixel or voxel within a desired object) have been developed.Typically, region growing technique start at a seed location and growthe region or volume from there. The seed location may define a graphicvalue or values that may be used in determining whether connected pixelsor voxels will be included in the region or volume being grown.Typically, a tolerance range or threshold(s) is provided to be used intesting graphic components of elements (pixels or voxels) connected tothe region or volume being grown to determine if the elements are to beadded to the region or volume. Typically, a user selects a seed locationas the starting location within an object from which a region (orvolume) will be automatically grown according to the particular regiongrowing technique being used, and specifies the tolerance orthreshold(s). The seed location specifies a “first element” of theregion or volume. One or more graphic components of connected elementsto the first element (pixels or voxels) are tested and, if the testedgraphic components satisfy the specified threshold(s), the connectedelements are added to the region or volume. Connected elements to theregion or volume are tested and excluded from or added to the growingregion or volume, and so on, until all connected elements have beentested and no more connected elements that satisfy the threshold testcan be found. Thus, the region or volume grows until all connectedelements (pixels or voxels) that pass the threshold test are found.

Medical images, and some images in other fields, often contain narrowconnecting pathways between objects in an image, which may introducedifficulties in segmenting the images. This is particularly problematicin medical imaging as structures like the vascular system areparticularly hard to segment as they often contain small connections orpathways to other organs, etc. Region growing techniques that simplylook at and test the graphic components of single connected pixels orvoxels tend to go down these pathways, and thus may grow regions orvolumes well beyond the boundaries of the desired objects.

Some region growing mechanisms that have been developed introduce theconcept of a radius around the test location (the current element undertest) that is applied in an attempt to prevent growing the region orvolume down an undesired narrow connection or path. Instead of testingthe graphic components of a single element under test to determine ifthe element meets the threshold requirements, all of the elements withinan area defined by the specified radius are tested. If some specifiednumber, e.g. a majority, 75%, or even all, of the elements within theradius-defined area meet the threshold requirements, then the element isadded to the region or volume being grown; otherwise, the element isexcluded from the region or volume. By requiring that the test area belarger than a single pixel or voxel, single pixel or voxel connectionsmay be avoided. Increasing the test radius results in a correspondingincreased requirement for the size of a connecting pathway, reducing thelikelihood that a region or volume will be grown down an undesired pathor connection.

A problem with a radius based test area for a region growing mechanismis that, if it is required that only a majority of elements in the testarea are within the tolerance range, then undesired elements may beincluded in the region or volume being grown, and narrow connections mayeven be crossed. If all elements within the test area must be within thetolerance range, then those issues may be avoided, but the region orvolume may only grow to within the radius distance of the edge of thedesired object in at least some places. This may result in an inset fromthe desired true edge of the object being selected; elements within thisedge inset will not be included in the region or volume, even if theelements meet the test criteria.

SUMMARY

Various embodiments of a method and apparatus for extending regions intwo-dimensional (2-D) image space or volumes in three-dimensional (3-D)image space that are generated by a region growing mechanism that uses atest area defined by a radius and that thus includes multiple elementsthat are tested. Instead of testing the graphic components of a singleelement under test to determine if the element meets the thresholdrequirements, the region growing mechanism tests all of the elementswithin an area defined by the specified radius. If all elements withinthe test area must be within the tolerance range of the test, then theregion or volume may only grow to within the radius distance of the trueedge of the desired object, thus leaving an edge inset.

Embodiments of a dilation mechanism may perform post-processing of aregion or volume generated by a test area-based region growing mechanismto correct for such an edge inset. The dilation mechanism performs amorphological dilate as described herein to expand the region or volumeto the proper edge of the desired object in the image data within thetolerance range of the threshold, and thus corrects for the inset errorintroduced by the test area radius used by the region growing mechanism.The dilation mechanism may be limited to extending the region or volumeto the radius distance from the edge of the original region or volumegenerated by the region growing mechanism.

Embodiments of the dilation mechanism implement a dilation method thatiterates over elements in at least a portion of the image data set (theset of all elements in the 2-D or 3-D image) and adds elements to theedge of the region, or rejects elements, in accordance with a set of oneor more rules or tests for inclusion in the region. The one or morerules or tests for inclusion may include, but are not limited to, adistance test, a connectivity test, and a threshold test.

Embodiments of the dilation mechanism may iterate over or otherwiseexamine at least a portion of the elements in the image data set tolocate elements that are connected to elements that are already includedin the region according to a connectivity rule being used. If an elementis found that is not included in the region and that is connected to anelement that is included in the region, and if the location of theelement, as determined by its coordinates in the image space, is withinthe distance of the radius from the edge of the original regiongenerated by the region growing mechanism, and if the graphiccomponent(s) of the element pass the threshold test, the element isadded to the region. Otherwise, the element is not added to the region.In addition, the element may be, but is not necessarily, excluded fromfurther testing to prevent the dilation mechanism from testing theelement again. Thus, iterations of the dilation mechanism may tend toadd one layer of elements to the edge of region, expanding the edge ofthe region from the original region out to the limit of the radius usedby the region growing mechanism to grow the original region.

Embodiments of the dilation mechanism may be implemented in imageprocessing systems that include region growing mechanisms for segmentingdesired objects in two-dimensional (2-D) and 3-dimensional (3-D) images.Note, however, that embodiments of the dilation mechanism as describedherein may be implemented to work with other types of image segmentationmechanisms that may leave edge insets or other gaps in located regionsor volumes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a two-dimensional (2-D) digital image.

FIG. 2 illustrates pixels in 2-D image space.

FIG. 3 illustrates a three-dimensional (3-D) digital image set.

FIGS. 4A and 4B illustrate voxels in 3-D image space.

FIG. 5 illustrates data flow in an image segmentation process thatimplements a test area-based region growing mechanism and a dilationmechanism to perform post-processing of a region or volume generated bythe region growing mechanism, according to one embodiment.

FIGS. 6A and 6B are graphical representations of exemplary test areasthat may be used by a region growing mechanism 110 in 2-D image space.

FIGS. 7A and 7B are graphical representations of exemplary test areasthat may be used by a region growing mechanism 110 in 3-D image space.

FIGS. 8A through 8H graphically represent an image segmentation processthat implements a region growing mechanism that uses a test area definedby a radius to grow a region and a dilation mechanism to extend theregion to correct for the edge inset left by the region growingmechanism, according to one embodiment.

FIG. 9 illustrates an image segmentation process that implements aregion growing mechanism and a dilation mechanism as described herein togrow a volume in 3-D image space, according to one embodiment.

FIG. 10 illustrates an exemplary user interface to an image segmentationprocess including a region growing mechanism and a dilation mechanism asdescribed herein.

FIG. 11 is a flowchart of an image segmentation method that includes atest area-based region growing mechanism and a dilation mechanismaccording to one embodiment.

FIG. 12 is a flowchart of a dilation method implemented by a dilationmechanism according to one embodiment.

FIG. 13 illustrates an exemplary computer system on which embodimentsmay be implemented.

While the invention is described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that the invention is not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit the invention tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present invention as defined by the appendedclaims. The headings used herein are for organizational purposes onlyand are not meant to be used to limit the scope of the description orthe claims. As used throughout this application, the word “may” is usedin a permissive sense (i.e., meaning having the potential to), ratherthan the mandatory sense (i.e., meaning must). Similarly, the words“include”, “including”, and “includes” mean including, but not limitedto.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of a method and apparatus for extending regions intwo-dimensional (2-D) image space or volumes in three-dimensional (3-D)image space that are generated by a region growing image segmentationmechanism that uses a test area defined by a radius and that thusincludes multiple elements that are tested. Note that, in 2-D imagespace, an element is one pixel; in 3-D image space, an element is onevoxel. Instead of testing the graphic components of a single elementunder test to determine if the element meets the threshold requirements,the region growing mechanism tests all of the elements within an areadefined by the specified radius. If all elements within the test areamust be within the tolerance range of the test, then the region orvolume may only grow to within the radius distance of the true edge ofthe desired object, thus leaving an edge inset.

Embodiments of a dilation mechanism as described herein may be used toperform post-processing of a region or volume to correct for such anedge inset. The dilation mechanism performs a morphological dilate asdescribed herein to expand the region or volume to the proper edge ofthe desired object in the image data within the tolerance range of thethreshold, and thus corrects for the inset error introduced by the testarea radius used by the region growing mechanism. The dilation mechanismmay be limited to extending the region or volume to the radius distancefrom the edge of the original region or volume generated by the regiongrowing mechanism.

Embodiments of the dilation mechanism may be implemented in imageprocessing systems that include region growing mechanisms for segmentingdesired objects in two-dimensional (2-D) and 3-dimensional (3-D) images.Note, however, that embodiments of the dilation mechanism as describedherein may be implemented to work with other types of image segmentationmechanisms that may leave edge insets or other gaps in located regionsor volumes.

FIG. 5 illustrates data flow in an image segmentation process thatimplements a dilation mechanism to perform post-processing of a regionor volume generated by a region growing mechanism to correct for an edgeinset introduced by a radius used in determining a test area around anelement under test by the region growing mechanism, according to oneembodiment.

Inputs to the region growing mechanism 110 may include, but are notlimited to, digital image data 100, a radius 102, threshold(s) 104, anda seed point 106. Image data 100 is the digital image data set that isto be segmented using embodiments of an image segmentation process asdescribed herein may be any type of digital images, and may be capturedvia any type of digital image capturing technology, as previouslydescribed. In particular, image data 100 may be medical images. Imagedata 100 may include a 2-D image or a 3-D image set. In particular,image data 100 may be medical 3-D image sets including a number of“slices” such as images generated by an MRI, CT Scan, or other 3-Dmedical imaging technology.

Radius 102 defines a radius value for the test area around an element(pixel or voxel) under test. Radius 102 represents a distance from theelement under test that is used to scribe a test area around the elementunder test. In one embodiment, a portion or all of the elements that arewithin the radius distance from the element under test by the regiongrowing mechanism 100 may be considered when testing the particularelement under test to determine if the element under test is to be addedto (or excluded from) the region or volume being grown. Note that, in a2-D image, the test area itself may be described as a region; in a 3-Dimage set, the test area may be described as a volume.

In one embodiment, radius 102 may be specified by a human operator.Alternatively, radius 102 may be a fixed value, or may be automaticallydetermined. A particular value for radius 102 may be specified ordetermined to match determining characteristics of image data 100 (e.g.,the width of any connecting pathways between objects in image data 100)and/or a particular value for radius 102 may be selected to work wellwith a general type of image data 100 (e.g., a radius may be selectedthat works well with medical images captured using a particular medicalimaging technology or captured at a particular resolution).

If, for example, the value for radius 100 is specified by a humanoperator, the value may be specified in standard units of measurement(in medical imaging, typically, metric units such as centimeters ormillimeters). The specified value may then be converted from the unitsof measurement into the number of elements that are contained withinthat distance at the resolution of the image data 100. For example, in a2-D image captured at resolution R, the 2-D image may include fivepixels for each millimeter on the x and y axes. If the specified valuefor radius 100 is one millimeter, then the value of radius 100 would be5. Note, however, that the x and y axes are typically but notnecessarily captured in the same resolution, and thus the radius valuemay need to be adjusted for the differing resolutions. For example, in a2-D image, a millimeter may include five pixels on the x axis and fourpixels on the y axis. The radius 100 for a 3-D image is similar,although it is more typical in 3-D images containing multiple slicessuch as those captured using MRI that the resolution on the z axis isdifferent than the resolution on the x and y axes. In general, thevoxels on the x and y axes in a 3-D image set are captured closertogether than the voxels across the slices on the z axis. Thus, theradius 100 for a 3-D image set may need to be adjusted to account forthe different resolution on the z axis to avoid the test area (volume)used by the region growing mechanism 110 from being distorted into anoblong shape, which would negatively impact the tests performed for acurrent voxel under test. For example, in a 3-D image set, a millimetermay include five pixels on the x axis, five pixels on the y axis, andtwo pixels on the z axis.

FIGS. 6A, 6B, 7A, and 7B are graphical representations of exemplary testareas that may be used by a region growing mechanism 110. FIG. 6Arepresents an exemplary test area for a 2-D image. Each square in FIG.6A represents one pixel. The shaded square represents the pixel undertest. In this example, there are five pixels within radius 102 (andthus, the diameter of the test area, in pixels, is eleven). Theexemplary test area in this example is roughly circular. However, theshape of the test area in FIG. 6A is exemplary, and that other shapesmay be used. For example, FIG. 6B illustrates an exemplary cross-shapedtest area with four pixels within radius 102.

FIG. 7A represents an exemplary test area for a 3-D image space. Eachcube in FIG. 7A represents one voxel. The voxel under test is notvisible, but is located at the center of the 3-D test area. Theexemplary test area in this example is roughly spherical. However, notethat the shape of the test area in FIG. 7A is exemplary, and that other3-D shapes may be used. Further note that the z axis represents the“slices” or images in the 3-D image set. In the exemplary test area ofFIG. 7A, there are five voxels within radius 102 on all three axes.

In 3-D image sets, the resolution on the z axis is typically not as“fine” as it is on the x and y axes of each individual image in theimage set. Therefore, it may be the case that there is a greaterdistance between voxels on the z axis than on the x and y axes. Toaccommodate for the resolution difference, the number of voxels withinthe radius 102 on the z axis may need to be adjusted. FIG. 7B representsan exemplary test area for a 3-D image space in which the resolution onthe z axis (between slices) is coarser than the resolution betweenvoxels on the x and y axes within each slice, and in which the number ofvoxels within the radius 102 on the z axis have been adjusted. In theexemplary test area of FIG. 7B, there are five voxels within radius 102on the x and y axes on each slice, but only two voxels within the radius102 on the z axis.

Referring again to FIG. 5, threshold(s) 104 represents one or morethreshold values that may be used by the region growing mechanism 110 inperforming tests on the graphic components of elements within image data100. As an example, in a color image stored in RGB (Red, Green, Blue)format, each element (e.g., pixel) in the image has a color associatedwith the element and represented by three color components (e.g., Red,Green, Blue (RGB)). Each color component may be stored in the pixel dataas a value. For example, in 32-bit color images, each color componentmay be stored as one byte, so each color component may have a (decimal)value between 0 and 255. The fourth byte may be used to store otherinformation about the element. Thus, for RGB images, threshold(s) 104may include values for one or more (typically, for all three) of thecolor components. As an example, for a 32-bit RGB image, threshold(s)104 may include, in decimal representation, the values (100, 150, 125)for the three color components.

Typically, objects to be segmented will not be uniform; in other words,the graphic components of elements within a desired object willtypically not be uniform. Instead, for example, the color at individualelements within the object may vary over some range. Thus, threshold(s)104 may also specify a tolerance range for the graphic component(s)under test, for example a tolerance range above or below one or more ofthe color component values of an element at the seed point 106. Forexample, a tolerance range of ±10%, or +10/−5%, or some other range maybe specified for values (100, 150, 125) of the three color components atthe seed point 106. As another example, a minimum range may bespecified, but no maximum range, or vice versa. For example, a minimumrange of 5% below one or more of the color component values of anelement at the seed point 106 may be specified, or alternatively amaximum range of 4% above one or more of the color component values ofan element at the seed point 106 may be specified. When the regiongrowing mechanism 110 is testing a particular element in image data 100,the graphic component(s) being tested of the element under test and of aportion or all of the elements within the test area defined by theradius 102 may be required to fall within the specified tolerance rangeof the graphic component(s) of the element at the seed point 106 for theparticular element under test to be added to the region 120 being grown.

Note that information in color images may be stored in other formatsthan RGB format, and in other color resolutions than 32-bit. Forexample, HSI (Hue, Saturation, Intensity) format is an alternative toRGB format. Thus, threshold(s) 104 may be used that are tailored to theparticular image format, for example, to the particular color format andto the particular color resolution.

In various embodiments, the value(s) of threshold 104 may be specifiedusing one or more of several methods. For example, a portion or all ofthe threshold 104 values may be manually entered via a user interface.As another example, a user may select a seed point 106 within image data100 as a starting point for the region growing mechanism 110. Threshold104 values to be used may then be programmatically obtained ordetermined from an element at or near the selected seed point 106, oralternatively may be programmatically determined from two or moreelements at or near the selected seed point 106, for example byaveraging the values of the graphic components for the two or moreelements that are to be used in testing elements in image data 100. Notethat, if the seed point 106 is used to determine the value ofthreshold(s) 104, then threshold(s) 104 may not be input into regiongrowing mechanism 110, but instead may actually be determined by regiongrowing mechanism 110 from the input seed point 106.

As mentioned above, seed point 106 represents the starting point for theregion growing mechanism 110 within image data 100. In variousembodiments, the seed point 106 may be specified using one or more ofseveral methods. In one embodiment, a user interface may be provided viawhich a user may select a seed point 106 within a displayed image. Forexample, the user may move the cursor over a desired starting locationin a displayed image for the region growing mechanism 110, for exampleusing a mouse, and select the location (e.g., by clicking a mousebutton). Note that in 3-D image sets, the user interface may provide amechanism whereby the user may view and select a particular slice, andselect a point within the slice, to specify the seed point 106. Furthernote that, when the user clicks or otherwise selects a seed point withina displayed image, a coordinate in the coordinate system of thedisplayed image on the display screen is selected. This coordinate isthen programmatically mapped into the coordinate system of the actualimage data 100 to locate an element that is to be used as the seed point106.

Region growing mechanism 110 receives the input digital image data 100,radius 102, threshold(s) 104, and seed point 106 and proceeds to growthe region 120 to thus segment (or locate) the desired object in imagedata 100. Note that, in FIG. 5, region 120 may represent either a regionin 2-D image space or a volume in 3-D image space. In one embodiment,beginning at an element in image data 100 indicated by seed point 106,region growing mechanism 110 iteratively tests elements in image dataaccording to the specified threshold(s) and, if a tested element passesthe threshold test, the element is added to the region 120. In oneembodiment, for an element under test to pass the threshold test, allelements within the test area defined by the radius 106 must also passthe threshold test.

In one embodiment, to initialize the region 120, an element in imagedata 100 indicated by seed point 106 is added to the region 120 as thefirst element in the region 120. Alternatively, to initialize the region120, two or more elements indicated by seed point 106 may be added asfirst elements in the region 120. In one embodiment, the threshold testis not applied to the initial elements that are added to region 120;thus, the region 120 will not be empty. Alternatively, the thresholdtest may be applied to the initial element(s). Note that, if the testarea-based test is applied to the initial element(s) that requires allelements within a test area defined by the radius 106 to pass thethreshold test for the element under test to be added to the region 120,the region 120 may be empty if the initial element(s) do not pass thetest.

After initializing the region 120, and assuming the region is not empty,connected elements to the region 120 (e.g., elements that are not in theregion 120 but that are connected to elements in the region 120according to the type of connectivity being used) that are available fortesting may then be iteratively tested. Note that elements may beexcluded from the set of elements available for testing, as describedbelow. However, note that an element excluded from testing is onlyexcluded from being the element under test, and is not excluded frombeing tested as one of the elements within the test area around acurrent element under test. Any of various methods may be used toindicate that an element is excluded from testing. For example, theelement may be added to a separate “mask” region that includes allelements that are determined to not be included in region 120 (e.g.,elements that fail the test area-based threshold test), and that arethus excluded from testing.

If a tested element passes the threshold test and all elements withinthe test area defined by the radius 106 pass the threshold test, theelement is added to the region 120. If the tested element does not passthe test area-based threshold test, the element may be excluded fromfurther testing so that the element is not tested again. The process maybe continued until no more elements that are both connected to theregion 120 and available for testing are found. The test area-basedregion growing process may be stated in exemplary outline form asfollows:

Initialize region 120 with an element or elements at the seed point 104.Repeat:   Get an element connected to the region and available fortesting.   If an available and connected element is found:     If theelement passes the test area-based threshold test:       Add the elementto the region 120.     Else if the element fails the test area-basedthreshold test:       Exclude the element from further testing. Until nomore elements that are both connected to the region 120 and availablefor testing are found.

Once region growing mechanism 110 completes, a region 120 representingthe segmentation of a desired object from image data 100 is an output.However, if the test area-based region growing mechanism 110 requiresthat all elements within the test area defined by radius 104 pass thethreshold test, the region 120 (which may be a region in 2-D space or avolume in 3-D space) may only grow to within the radius 104 distance ofthe edge of the desired object in at least some places. This may resultin an inset from the desired true edge of the object being segmented;elements within this edge inset will not be included in the region 120,even if the graphic components of the individual elements lie within thetolerance range of the threshold test. A test area defined by radius 104that includes one or more elements beyond the edge of the desired objectmay fail the test area-based test if one or more of the elements in thetest area do not pass the threshold test, and thus the element undertest will not be included in the region 120 even if the element undertest passes the threshold test.

Thus, embodiments of a dilation mechanism 130 may be used to performpost-processing of the original region 120 to correct for such an edgeinset. The dilation mechanism 130 may receive, but is not limited to,image data 100, region 120, radius 102, and threshold(s) 104 as input,and may output, but is not limited to, expanded region 140. Again notethat region 120 and region 140 may represent either a region in 2-Dimage space or a volume in 3-D image space. The dilation mechanism 130performs a morphological dilate as described below to expand theoriginal region 120 to the proper edge of the desired object in theimage data 100 within the tolerance range of the threshold(s) 104,outputting expanded region 140, and thus corrects for the inset errorintroduced by the test area radius 102 used by the region growingmechanism 110. The dilation mechanism 130 may be limited to expandingregion 140 to no more than the radius 102 distance from the edge of theoriginal region 120 generated by the region growing mechanism 110.Limiting the expansion to no more than the radius 102 distance from theedge of the original region 120 may help prevent the region 140 frombeing grown down undesirable connective pathways.

Embodiments of the dilation mechanism 130 implement a dilation methodthat iterates over elements in at least a portion of the image data 100set (the set of all elements in the 2-D or 3-D image) and adds elementsto the edge of the region 140, or rejects elements, in accordance with aset of one or more rules or tests for inclusion in the region 140. Inone embodiment, each iteration may expand the edge of the region 140 byone element. The one or more rules or tests for inclusion may include,but are not limited to:

-   -   A distance test: Elements that are beyond the distance of the        radius 104 used by the region growing mechanism 110 from the        edge of the original region 120 generated by the region growing        mechanism 110 are not added to the region 140.    -   A connectivity test: Elements that are not connected to an        element in the region 140 are not added to the region 140. Note        that determining whether an element is connected to another        element depends on the type of connectivity being used. For        example, in 2-D image space, four-connectivity or        eight-connectivity may be used. In 3-D image space,        six-connectivity, 26-connectivity, or possibly some other        connectivity level or rule may be used.    -   A threshold test: Elements for which one or more of the        associated graphic components do not pass the threshold test for        the region are not added to the region or volume. In one        embodiment, the threshold test and threshold 104 value(s) used        by the region growing mechanism 100 to grow the original region        110 are also used by the dilation mechanism 130. However, the        dilation mechanism 130 performs the threshold test on individual        elements, and does not use a test area defined by the radius 102        as does the region growing mechanism 110.

In other words, embodiments of the dilation mechanism 130 may iterateover or otherwise examine at least a portion of the elements in theimage data 100 set to locate elements that are connected to elementsthat are already included in the region 140 according to a connectivityrule being used. If an element is found that is not included in theregion 140 and that is connected to an element that is included in theregion 140 (according to the type of connectivity being used), and ifthe location of the element, as determined by its coordinates in theimage space, is within the distance of the radius 102 from the edge ofthe original region 120 generated by the region growing mechanism 110,and if the graphic component(s) of the element pass the threshold test,the element is added to the region 140. Otherwise, the element is notadded to the region 140. In addition, the element may be, but is notnecessarily, excluded from further testing to prevent the dilationmechanism 130 from testing the element again. Thus, iterations of thedilation mechanism 130 may tend to add one layer of elements to the edgeof region 140, expanding the edge of the region 140 from the originalregion 120 out to the limit of the radius 102 used by the region growingmechanism 110 to grow the original region 120.

In one embodiment, a subset of the elements in the image space to betested in iterations of the dilation mechanism 130 may be determinedbefore beginning the iterations. This limits the elements to be testedby the dilation mechanism 130 to a subset of the elements in image data100. In one embodiment, a copy of original region 120 may be generatedas an initial region 140, which may then be expanded by the dilationmechanism 130.

Note that the edge of a region may be considered a curve, and the edgeof a volume may be considered a surface. In one embodiment, for regionsin 2-D space, before iterating over the set of elements to be tested, acurve may be fit to the edge of an original region (region 120) found bythe region growing mechanism 110. Similarly, for volumes in 3-D space, asurface may be fit to the edge of an original volume (region 120) foundby the region growing mechanism 110. The determined curve or surface maythen be used during iterations of the dilation mechanism 130 inperforming the distance test for elements being tested. Mathematicalmethods for determining the minimum distance of a point to a curve in2-D space or to a surface in 3-D space are well-known and available, andany such method may be implemented by dilation mechanism 130 to performthe distance test.

For example, a set of pixels constituting the edge or boundary of aregion in 2-D image space may be determined. One method of finding anedge or boundary of a region is to exhaustively iterate over the pixelsthat are included in the region 120 found by the region growingmechanism 110, testing each pixel to determine if the pixel has at leastone connecting pixel that is not included in the region 120. Any suchpixel would be considered to be on the edge of the region 120. Note thatfour-connectivity may be used in this test so that diagonally-connectedpixels are not considered. Another method of finding an edge or boundaryof a region is to locate one pixel on the edge of the region, and thenapply a boundary tracking technique to find the boundary. Other imageprocessing methods of finding an edge or boundary of a region areavailable, and any such method may be used in various embodiments.

Once the set of edge pixels (the boundary) is determined, a curve may befit to the set of edge pixels. In one embodiment, the curve may beexpressed as a polynomial. Any one of several mathematical methods tofind the minimum distance to the curve, expressed as a polynomial, maythen be used in the distance test. Note that other mathematical methodsthan the polynomial method may be used to find the minimum distance of apoint (an element) to a curve (the edge of the region 120).

Note that similar mathematical and/or morphological methods to thosedescribed above, or other methods, may be used or adapted to find thesurface of a volume in 3-D image space and to find the minimum distanceof a voxel to the surface of the volume.

In one embodiment, the determined curve or surface may also be used indetermining a subset of the elements (a search area) in the image space(image data 100) to be tested in iterations of the dilation mechanism130. For example, minimum and maximum points on the x and y axes of thecurve fit to a region may be found. Similarly, minimum and maximumpoints on the x, y, and z axes of the surface fit to a volume may befound. The radius, or radius+1, may be added to or subtracted from theminimum/maximum points as appropriate to find an outer boundary for asearch area. Note that the region 120 may be used to “mask” the searcharea—only elements that are in the search area but not in region 120 maybe included in the search area.

In one embodiment, if an element initially fails the connectivity testand thus is not initially added to region 140, the element may be leftin a set of elements to be tested and thus may later be reconsidered forinclusion in region 140 if connecting elements to the element are addedto the region 140. In one embodiment, if an element initially fails thedistance test or threshold test, the element may be excluded from theset of elements being tested so that the element is not retested inlater iterations.

FIGS. 8A through 8H graphically represent an image segmentation processthat implements a region growing mechanism that uses a test area definedby a radius to grow a region and a dilation mechanism to extend theregion to correct for the edge inset left by the region growingmechanism, according to one embodiment. Note that the image segmentationprocess illustrated in FIGS. 8A through 8H may be used to grow regionsin 2-D image space or volumes in 3-D image space; therefore, the regionsand test areas depicted in these Figures may represent either regions in2-D image space or volumes in 3-D image space.

FIGS. 8A through 8D graphically represent the operation of a regiongrowing mechanism such as region growing mechanism 110 of FIG. 5. FIG.8A shows the outline of a desired object 200 in image space. Note thenarrow connecting pathway 201. A user of the image segmentation processmay desire that the region growing mechanism 110 not grow the regiondown pathway 201 or similar connecting pathways. Thus, a test area-basedregion growing mechanism 110 may be used. A radius 102 that may be usedto define the test areas 208 may be specified, for example via userinput to a user interface of the image segmentation process. The radius102 may be specified so that the diameter of the test area 208 definedby the radius 102 is greater than the width of pathway 201. A seed point106 may be specified using one or more of several methods as previouslydescribed for FIG. 5, for example via user input to a user interface ofthe image segmentation process. Seed point 106 represents the startingpoint for the region growing mechanism within object 200. Thresholdvalues to be used in testing the graphic components of elements todetermine if the elements qualify for inclusion in a region being grownmay be determined from an initial element or elements at or near thespecified seed point 106, or alternatively may be otherwise specified ordetermined, for example via user input to a user interface of the imagesegmentation process.

The region growing mechanism 110 may initialize a region to be grown(region 120, not shown in FIG. 8A). In one embodiment, one or moreelements at or near the seed point 106 may be added as initial elementsin the region 120. In one embodiment, a test area-based threshold testis applied to an initial element at seed point 106. The radius is usedto determine a test area 208A around the element. In one embodiment, allelements within this test area 208A must also pass the threshold testfor the element under test (the initial element at the seed point) to beadded to the region. If the element does not pass the test area-basedthreshold test, the element is not added to the region 120. If theelement does pass the test, the element is added to the region. Notethat in one embodiment, the initial element may not be required to passthe test area-based threshold test, and is simply added to the region120 to initialize the region.

FIGS. 8B and 8C graphically illustrate the growing of region 120 usingthe test area-based region growing mechanism 110. After initializing theregion to be grown (region 120, not shown in FIGS. 8A through 8C), andassuming the region is not empty, connected elements to the region 120(e.g., elements that are not in the region 120 but that are connected toelements in the region 120 according to the type of connectivity beingused) and that are available for testing may then be iteratively testedusing the test area-based threshold test. If a tested element passes thethreshold test and all elements within the test area 208 defined by theradius 102 pass the threshold test, the element is added to the region120. If the tested element does not pass the test area-based thresholdtest, the element may be excluded from further testing so that theelement is not tested again. The process may be continued until no moreelements that are both connected to the region 120 and available fortesting are found. FIG. 8B illustrates a few test areas 208B early inthe region growing mechanism. Region 120 at this point would roughlyencompass the center points of the test areas 208B. In this example, theelements under test in the test areas 208B would all pass the testarea-based threshold test. FIG. 8C illustrates more test areas 208Clater in the region growing mechanism. Region 120 at this point wouldroughly encompass the center points of the test areas 208C. In thisexample, the elements under test in the test areas 208C would all passthe test area-based threshold test. FIG. 8D graphically illustrates theregion being grown at or near the conclusion of the operation of theregion growing mechanism. Test areas 208D, shown with their bordersbolded, represent test areas that have passed the test area-basedthreshold test. The elements under test in test areas 208D (i.e., thecenter element in each test area 208D) are all added to region 120. Testareas 208E, shown with their borders not bolded, represent test areasthat have failed the test area-based threshold test. These test areas208E all include at least one element that is outside the actual edge ofobject 200, and thus fail the test area based threshold test, thusexcluding the element under test from being added to region 120.Essentially, any element under test that is within the radius 102distance of the actual edge of the desired object 200 may fail the testarea-based threshold test if the test area 208 around the elementincludes one or more elements that fail the threshold test, even if theelement under test passes the threshold test.

FIG. 8E graphically illustrates the resultant region 120 that isgenerated by the test area-based region growing mechanism 110. Note theedge inset 210 from the actual edge of the desired object 200 that isintroduced by the use of a test area-based threshold test. This edgeinset 210 may be inset from the actual edge of the desired object byapproximately the radius distance.

FIGS. 8F through 8H graphically illustrate the post-processing performedby a dilation mechanism, such as dilation mechanism 130 of FIG. 5, tocorrect for such an edge inset 210, according to one embodiment.Embodiments of the dilation mechanism 130 implement a dilation methodthat iterates over elements in at least a portion of the image data set(the set of all elements in the 2-D or 3-D image) and adds elements tothe edge of the region being grown, or rejects elements, in accordancewith a set of one or more rules or tests for inclusion in the region.The one or more rules or tests were previously described for FIG. 5.

In one embodiment, the dilation mechanism 130 may iterate over orotherwise examine at least a portion of the elements in the image dataset to locate elements that are connected to elements that are alreadyincluded in the region being grown according to a connectivity rulebeing used. If an element is found that is not included in the regionand that is connected to an element that is included in the region, andif the location of the element, as determined by its coordinates in theimage space, is within the radius 102 distance from the edge of theoriginal region generated by the region growing mechanism 110, and ifthe graphic component(s) of the element pass the threshold test, theelement is added to the region being grown. Otherwise, the element isnot added to the region being grown. In addition, the element may be,but is not necessarily, excluded from further testing to prevent thedilation mechanism 130 from testing the element again. Thus, iterationsof the dilation mechanism 130 may tend to add one layer of elements tothe edge of the region being grown, expanding the edge of the regionfrom the original region generated by the region growing mechanism outto the limit of the radius 102 used by the region growing mechanism 110to grow the original region.

In FIG. 8F, a first iteration of dilation mechanism 130 has added onelayer of elements 212 within edge inset 210 to the original region 120generated by the region growing mechanism 110. In FIG. 8G, furtheriterations of dilation mechanism 130 have continued to add elements 212to region 120, growing the region out from the edge of the originalregion 120 to the extent of the radius 102, thus filling the edge inset210. Note, however, that the rule that excludes elements 212 that failthe distance test from being added to the region 120 prohibits theregion 120 from being grown down the connecting pathway 201. FIG. 8Hgraphically illustrates the resultant expanded region 140 generated fromthe original region 120 by the dilation mechanism 130. Note that theregion 140 closely matches the true edge of the desired object 200, butdoes not extend down pathway 201.

FIG. 9 illustrates an image segmentation process that implements aregion growing mechanism and a dilation mechanism as described herein togrow a volume in 3-D image space, according to one embodiment. FIG. 9further illustrates the volume generated by the 3-D process used torender a 3-D representation of an object of interest. Image data 300represents a 3-D digital image data set that is to be segmented usingembodiments of an image segmentation process as described herein. Imagedata 300 may be any type of 3-D digital images from any field, and maybe captured via any type of 3-D digital image capturing technology. Asan example, image data 300 may be medical 3-D image sets including anumber of “slices” such as images generated by an MRI, CT Scan, or other3-D medical imaging technology.

Region growing mechanism 110 may operate to grow an original volume aspreviously described in FIG. 5 through FIG. 8H (note again that region120 and region 140 in FIGS. 5 and FIGS. 8A through 8H may representvolumes in 3-D image space). Dilation mechanism 130 may operate toexpand the original region 120 to generate expanded region 140 by addingconnected elements that pass the threshold test to the volume up to thelimit of the radius used by the region growing mechanism 110. Note thateach image or “slice” essentially includes a 2-D region generated by theimage segmentation process; the combination of the 2-D regionsconstitute the volume (region 140) in 3-D image space. The 2-D regionsconstituting the volume may be thought of as “slices” of the volume. A3-D rendering process 302 may then receive region 140 as input andrender the input into a 3-D representation 304 of the object ofinterest.

FIG. 10 illustrates an exemplary user interface to an image segmentationprocess including a region growing mechanism and a dilation mechanism asdescribed herein. Note that display 500 is exemplary, and is notintended to be limiting. Display 500 may include an image 500. Image 500is a display of the digital image data that is to be segmented usingembodiments of an image segmentation process as described herein. Image500 may be a 2-D image or a “slice” (one image) from a 3-D image set. In3-D image sets, the user interface may provide a mechanism whereby theuser may select a particular slice to be displayed as image 500.Further, in 3-D image sets, the user interface may concurrently displaytwo or more slices from the image set.

Seed point 506 represents the starting point for the region growingmechanism within image 500. In one embodiment, the user interface mayallow the user to select the seed point 506 within the displayed image500. For example, the user may move the cursor over a desired startinglocation in the displayed image 500, for example using a mouse or othercursor control device, and then select the location (e.g., by clicking amouse button). When the user clicks or otherwise selects a seed point506 within displayed image 500, a coordinate in the coordinate system ofthe displayed image on the display screen is selected. This coordinatemay then be programmatically mapped into the coordinate system of theactual image data to locate an element that is to be used as the seedpoint 506.

Relevant graphic values for the element at the selected seed point 506may be displayed on the user interface. For example, exemplary display500 includes a field that textually displays the RGB values for theelement at the seed point 506. The user interface may also include oneor more user interface items for entering threshold values, the radiusand possibly other variables. Exemplary display 500 includes two textentry boxes for entering a minimum and maximum value for a range aroundthe graphic component(s) to be tested. In this example, the minimum andmaximum values may be entered as percentages. One of ordinary skill inthe art will recognize that other methods for specifying the thresholdvalues may be used. Exemplary display 500 includes a text entry box forspecifying a radius to be used in defining a test area by the regiongrowing mechanism and by the dilation mechanism as a limit on expansionof the region. One of ordinary skill in the art will recognize thatother methods for specifying the radius may be used. As previouslydescribed, the radius may be entered in standard units of measurement(e.g., millimeters) and then converted to elements per unit, oralternatively may be entered as the number of elements. Note that, as analternative to specifying a radius for the test area, a diameter for thetest area may be specified.

The user interface may include a user interface item for launching theimage segmentation process. For example, display 500 includes auser-selectable “Segment” button 508 that launches the imagesegmentation process with the specified seed point 506, radius, andthreshold values as input.

FIG. 11 is a flowchart of an image segmentation method that includes atest area-based region growing mechanism and a dilation mechanismaccording to one embodiment. As indicated at 400, image data may bereceived. Image data may be a 2-D image or a 3-D image set, aspreviously described. As indicated at 402, a seed point, thresholdvalue(s), and radius may be specified, for example via user input to auser interface such as the exemplary user interface of FIG. 10.

As indicated at 404, a test area-based region growing mechanism may beinitiated that may grow a region starting at the seed point and using athreshold test in accordance with the threshold value(s) on test areasdefined by the radius. Note that the region may be a region in 2-D imagespace or a volume in 3-D image space, and the test area may be a regionin 2-D image space or a volume in 3-D image space. In one embodiment, atest area containing two or more elements may be defined by the radiusaround a current element under test. In one embodiment, all of theelements in the test area must pass the threshold test for the elementunder test to be added to the region. This process is repeated to testconnected elements to the region until the set of elements available fortesting are exhausted. This may prevent the region growing mechanismfrom extending the region being grown down undesirable narrow pathways,but may have the side effect of leaving an edge inset between the edgeof the region and the true edge of the object of interest.

To correct for the edge inset, a dilation mechanism may performpost-processing of the original region generated by the region growingmechanism. As indicated at 406, the dilation mechanism may expand theregion by applying the threshold test on single elements connected tothe region being grown and limited to the distance of the radius fromthe edge of the original region. In other words, only elements that arewithin the distance of the radius from the edge of the original regiongenerated by the region growing mechanism may be (but are notnecessarily) added to the region being grown. Note that 406 is describedin more detail in FIG. 12.

FIG. 12 is a flowchart of a dilation method implemented by a dilationmechanism according to one embodiment. Note that FIG. 12 provides moredetail to item 408 of FIG. 11. As indicated at 500, a dilation mechanismmay receive an original region generated by a test area-based regiongrowing mechanism, one or more threshold values, and a radius that wasused by the region growing mechanism. Note that the original region maybe a region in 2-D image space or a volume in 3-D image space. Thedilation mechanism may iterate over a set of elements available fortesting to expand the original region (or a copy of the original region)to fill the edge inset left by the test area-based region growingmechanism. As indicated at 502, the dilation mechanism may attempt tofind, in a set of elements to be tested, an element connected to theregion being grown (according to the type of connectivity being used)and within radius distance of the edge of the original region generatedby the region growing mechanism. If no such element is found, thedilation mechanism is done. If an element is found that is connected tothe region being grown and that is within the radius distance of theedge of the original region, the dilation mechanism may then test thegraphic component(s) of the element against the input thresholdvalue(s), as indicated at 506. At 508, if the element passes thethreshold test, the element is added to the region being grown, asindicated at 510. At 512, if there are more elements in the set ofelements available for testing, the dilation mechanism may return to 502to find a next element for testing. If there are no more elementsavailable for testing, the dilation mechanism is done.

Bitmaps and Similar Structures

Embodiments of the image segmentation process including a dilationmechanism 130 as described herein may need to temporarily or permanentlystore image processing information for various purposes. Note that onemethod that is commonly used in image processing to store information,such as the edge or boundary information for regions and volumes asdescribed above, is by using what are commonly referred to in imageprocessing as bitmaps, or similar data structures. A bitmap isessentially, for a 2-D image, an h*w storage space, where h is theheight of the image and w the width. For a 3-D image, a bitmap would bean h*w*d storage space, where h is the height of the images, w thewidth, and d the depth (the total number of images or “slices” in the3-D data set). Whereas the actual color or grayscale image requiresmultiple bytes to store graphic and other information for each imageelement, a bitmap uses only one bit to store information about aparticular element location. For example, an exemplary 2-D image withthe dimensions 1024 (w)*768 (h) would require the allocation of 786,432bits (98,304 bytes) for a bitmap. An exemplary 3-D image set containingfive “slices” each represented by one such 1024*768 image would requirethe allocation of 5*786,432 bits, or 491,520 bytes.

Indexing into a bitmap is simple. For example, in the exemplary 1024*768image, there are 768 “rows”, typically indexed as rows 0 to 767, andthere are 1024 “columns”, typically indexed as columns 0 to 1023. The786,432 bits, each representing a particular pixel, are typicallyindexed as bits 0 to 786,431. Note that information may be stored in abitmap in column order or in row order. If the bitmap is in row order,then the first 1024 bits represent the first row, the second 1024 bitsthe second row, and so on. If the bitmap is in column order, then thefirst 768 bits represent the first column, the second 768 bits representthe second column, and so on. To index to a particular bit correspondingto a particular pixel, using row order as an example, an exemplaryindexing formula is simply:

(row number*width)+column number

where row number and column number are obtained from the coordinates ofthe particular pixel, and assumes that the pixel coordinates are alsozero-based. Width is the width of the image, and thus is the same as thenumber of columns; it could also be referred to as the row length. Thus,a particular bit in a bitmap ImageBitmap may be indexed by somethingsimilar to the following piece of pseudocode:

ImageBitmap[(pixel.row_number*num_columns)+pixel.column_number]

This indexing may be used, for example, to set or clear a particular bitin the bitmap, or to read a particular bit from the bitmap. Also notethat a similar indexing method that takes into account the depth (i.e.,slice number) may be used to index into bitmaps or similar structuresfor 3-D image sets. The slices may, for example, be indexed from 0 to N.To index to a particular bit corresponding to a particular voxel in the3-D image set, using row order as an example, an exemplary indexingformula is simply:

((slice number*slice size)+(row number*width))+column number

where slice number represents the particular (2-D) image in the 3-D dataset that includes the voxel, and slice size represents the number ofvoxels in each slice (e.g., 786,432, in a 1024*768 image or “slice”).

Note that the same or similar indexing methods may be used to addressthe elements in the images, for example the pixels in 2-D images orvoxels 3-D image sets.

Note that similar data structures to the bitmaps described above butthat use more space than one bit to store information for each imageelement location may be used. For example, information may be stored foreach element in an image in two bits, half a byte, a byte, two bytes,and so on. The indexing methods described above may be modified toaddress bits, portions of bytes, bytes, or so on, in such datastructures.

In addition to the boundary or edge information, a bitmap or similardata structure may be used to mark whether particular pixels areincluded in (or excluded from) a region being grown, to mark whetherparticular voxels are included in (or excluded from) a volume beinggrown, and for other purposes during the region growing mechanism and/ordilation process as described herein. Bitmaps or similar data structuresmay also be used for many other purposes; for example, a bitmap storingboundary information for a region or volume may be used to draw arepresentation of the boundary on a display screen or printout, a bitmapmay be used in extracting data (e.g., a subset of pixels or voxels) froman image data set, and so on.

Exemplary System

Various embodiments of a an image segmentation process including aregion growing mechanism and dilation mechanism as described herein maybe executed on one or more computer systems, which may interact withvarious other devices. One such computer system is illustrated by FIG.13. In the illustrated embodiment, computer system 700 includes one ormore processors 710 coupled to a system memory 720 via an input/output(I/O) interface 730. Computer system 700 further includes a networkinterface 740 coupled to I/O interface 730, and one or more input/outputdevices 750, such as cursor control device 760, keyboard 770, audiodevice 790, and display(s) 780. In some embodiments, it is contemplatedthat embodiments may be implemented using a single instance of computersystem 700, while in other embodiments multiple such systems, ormultiple nodes making up computer system 700, may be configured to hostdifferent portions or instances of embodiments. For example, in oneembodiment some elements may be implemented via one or more nodes ofcomputer system 700 that are distinct from those nodes implementingother elements.

In various embodiments, computer system 700 may be a uniprocessor systemincluding one processor 710, or a multiprocessor system includingseveral processors 710 (e.g., two, four, eight, or another suitablenumber). Processors 710 may be any suitable processor capable ofexecuting instructions. For example, in various embodiments, processors710 may be general-purpose or embedded processors implementing any of avariety of instruction set architectures (ISAs), such as the x86,PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. Inmultiprocessor systems, each of processors 710 may commonly, but notnecessarily, implement the same ISA.

System memory 720 may be configured to store program instructions and/ordata accessible by processor 710. In various embodiments, system memory720 may be implemented using any suitable memory technology, such asstatic random access memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions and data implementingdesired functions, such as those described above for an imagesegmentation process including a region growing mechanism and dilationmechanism as described herein, are shown stored within system memory 720as program instructions 725 and data storage 735, respectively. In otherembodiments, program instructions and/or data may be received, sent orstored upon different types of computer-accessible media or on similarmedia separate from system memory 720 or computer system 700. Generallyspeaking, a computer-accessible medium may include storage media ormemory media such as magnetic or optical media, e.g., disk or CD/DVD-ROMcoupled to computer system 700 via I/O interface 730. Programinstructions and data stored via a computer-accessible medium may betransmitted by transmission media or signals such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link, such asmay be implemented via network interface 740.

In one embodiment, I/O interface 730 may be configured to coordinate I/Otraffic between processor 710, system memory 720, and any peripheraldevices in the device, including network interface 740 or otherperipheral interfaces, such as input/output devices 750. In someembodiments, I/O interface 730 may perform any necessary protocol,timing or other data transformations to convert data signals from onecomponent (e.g., system memory 720) into a format suitable for use byanother component (e.g., processor 710). In some embodiments, I/Ointerface 730 may include support for devices attached through varioustypes of peripheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some embodiments, the function of I/Ointerface 730 may be split into two or more separate components, such asa north bridge and a south bridge, for example. In addition, in someembodiments some or all of the functionality of I/O interface 730, suchas an interface to system memory 720, may be incorporated directly intoprocessor 710.

Network interface 740 may be configured to allow data to be exchangedbetween computer system 700 and other devices attached to a network,such as other computer systems, or between nodes of computer system 700.In various embodiments, network interface 740 may support communicationvia wired or wireless general data networks, such as any suitable typeof Ethernet network, for example; via telecommunications/telephonynetworks such as analog voice networks or digital fiber communicationsnetworks; via storage area networks such as Fibre Channel SANs, or viaany other suitable type of network and/or protocol.

Input/output devices 750 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or retrieving data by one or more computer system 700. Multipleinput/output devices 750 may be present in computer system 700 or may bedistributed on various nodes of computer system 700. In someembodiments, similar input/output devices may be separate from computersystem 700 and may interact with one or more nodes of computer system700 through a wired or wireless connection, such as over networkinterface 740.

As shown in FIG. 13, memory 720 may include program instructions 725,configured to implement embodiments of an image segmentation processincluding a region growing mechanism and dilation mechanism as describedherein as described herein, and data storage 735, comprising variousdata accessible by program instructions 725. In one embodiment, programinstructions 725 may include software elements of an image segmentationprocess including a region growing mechanism and dilation mechanism asillustrated in FIG. 5 and in FIG. 9. Data storage 735 may include datathat may be used in some embodiments. In other embodiments, differentsoftware elements and data may be included.

Those skilled in the art will appreciate that computer system 700 ismerely illustrative and is not intended to limit the scope of the imagesegmentation process including a region growing mechanism and dilationmechanism as described herein. In particular, the computer system anddevices may include any combination of hardware or software that canperform the indicated functions, including computers, network devices,internet appliances, PDAs, wireless phones, pagers, etc. Computer system700 may also be connected to other devices that are not illustrated, orinstead may operate as a stand-alone system. In addition, thefunctionality provided by the illustrated components may in someembodiments be combined in fewer components or distributed in additionalcomponents. Similarly, in some embodiments, the functionality of some ofthe illustrated components may not be provided and/or other additionalfunctionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 700 may be transmitted to computer system700 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link. Various embodiments mayfurther include receiving, sending or storing instructions and/or dataimplemented in accordance with the foregoing description upon acomputer-accessible medium. Accordingly, the present invention may bepracticed with other computer system configurations.

CONCLUSION

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc. As well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The various methods as illustrated in the Figures and described hereinrepresent exemplary embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. The orderof method may be changed, and various elements may be added, reordered,combined, omitted, modified, etc.

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended that the invention embrace all such modifications and changesand, accordingly, the above description to be regarded in anillustrative rather than a restrictive sense.

1. A computer-implemented method, comprising: generating a regioncomprising a subset of image elements within an object of interest in animage data set, wherein the region is generated by an image segmentationmechanism that leaves an edge inset between the boundary of the regionand the true edge of the object of interest; and extending the regiongenerated by the image segmentation mechanism to generate an extendedregion including at least a portion of image elements in an area definedby the edge inset; wherein said extending the region comprises limitingthe extension of the region to a specified distance from the outerboundary of the original region generated by the image segmentationmechanism.
 2. The method as recited in claim 1, wherein said extendingthe region generated by the image segmentation mechanism furthercomprises: for each element in a set of image elements to be tested thatincludes at least the image elements in the area defined by the edgeinset: getting an element that is connected to at least one elementcurrently in the extended region, wherein the extended region includesthe elements in the original region generated by the image segmentationmechanism and all elements that have been added to the original region;and adding the element to the extended region if the element is withinthe specified distance from the boundary of the original regiongenerated by the image segmentation mechanism and if one or more graphiccomponents of the element are within a specified range of one or morespecified thresholds for the graphic components.
 3. The method asrecited in claim 1, wherein said extending the region generated by theimage segmentation mechanism further comprises: for each element in aset of image elements to be tested that includes at least the imageelements in the area defined by the edge inset: applying a connectivitytest to the element, wherein said connectivity test determines if theelement is connected to at least one element currently in the extendedregion, wherein the extended region includes the original regiongenerated by the image segmentation mechanism and all elements that havebeen added to the original region; applying a distance test to theelement if the element passes the connectivity test, wherein saiddistance test determines if the element is within the specified distancefrom the boundary of the original region generated by the imagesegmentation mechanism; applying a threshold test to the element if theelement passes the connectivity test and the distance test, wherein saidthreshold test determines if one or more graphic components of theelement are within a specified range of one or more specified thresholdsfor the graphic components; and adding the element to the extendedregion if the element passes the threshold test.
 4. The method asrecited in claim 1, wherein said image segmentation mechanism is a testarea-based region growing mechanism, wherein a test area is defined by aspecified radius, and wherein said test area-based region growingmechanism adds elements to a region being grown using a threshold testthat determines if one or more graphic components of an element undertest and of all elements within a test area defined by the radius aroundthe element under test are within a specified range of one or morespecified thresholds for the graphic components.
 5. The method asrecited in claim 4, wherein the specified distance is the radius used bythe test area-based region growing mechanism.
 6. The method as recitedin claim 1, wherein the image data set is a two-dimensional image,wherein the image elements are pixels, and wherein the region is atwo-dimensional region within the two-dimensional image.
 7. The methodas recited in claim 6, wherein the two-dimensional image is a medicalimage.
 8. The method as recited in claim 1, wherein the image data setis a three-dimensional image set comprising a plurality of slices,wherein each slice is represented by a two-dimensional image, whereinthe image elements are voxels, and wherein the region and the extendedregion are three-dimensional volumes within the three-dimensional imageset.
 9. The method as recited in claim 8, wherein the three-dimensionalimage set is a medical image.
 10. The method as recited in claim 8,wherein the three-dimensional image set is captured using one ofMagnetic Resonance Imaging (MRI), computed tomography (CT), positronemission tomography (PET), ultrasound, and X-Ray.
 11. A system,comprising: at least one processor; and a memory comprising programinstructions, wherein the program instructions are executable by the atleast one processor to implement: an image segmentation mechanismconfigured to generate a region comprising a subset of image elementswithin an object of interest in an image data set, wherein the imagesegmentation mechanism leaves an edge inset between the boundary of theregion and the true edge of the object of interest; and a dilationmechanism configured to extend the region generated by the imagesegmentation mechanism to generate an extended region including at leasta portion of image elements in an area defined by the edge inset;wherein said dilation mechanism is further configured to limit theextension of the region to a specified distance from the outer boundaryof the original region generated by the image segmentation mechanism.12. The system as recited in claim 11, wherein, to extend the regiongenerated by the image segmentation mechanism, the dilation mechanism isfurther configured to: for each element in a set of image elements to betested that includes at least the image elements in the area defined bythe edge inset: get an element that is connected to at least one elementcurrently in the extended region, wherein the extended region includesthe elements in the original region generated by the image segmentationmechanism and all elements that have been added to the original region;add the element to the extended region if the element is within thespecified distance from the boundary of the original region generated bythe image segmentation mechanism and if one or more graphic componentsof the element are within a specified range of one or more specifiedthresholds for the graphic components.
 13. The system as recited inclaim 11, wherein, to extend the region generated by the imagesegmentation mechanism, the dilation mechanism is further configured to:for each element in a set of image elements to be tested that includesat least the image elements in the area defined by the edge inset: applya connectivity test to the element, wherein said connectivity testdetermines if the element is connected to at least one element currentlyin the extended region, wherein the extended region includes theoriginal region generated by the image segmentation mechanism and allelements that have been added to the original region; apply a distancetest to the element if the element passes the connectivity test, whereinsaid distance test determines if the element is within the specifieddistance from the boundary of the original region generated by the imagesegmentation mechanism; apply a threshold test to the element if theelement passes the connectivity test and the distance test, wherein saidthreshold test determines if one or more graphic components of theelement are within a specified range of one or more specified thresholdsfor the graphic components; and add the element to the extended regionif the element passes the threshold test.
 14. The system as recited inclaim 11, wherein said image segmentation mechanism is a test area-basedregion growing mechanism, wherein a test area is defined by a specifiedradius, and wherein said test area-based region growing mechanism isconfigured to add elements to a region being grown using a thresholdtest that determines if one or more graphic components of an elementunder test and of all elements within a test area defined by the radiusaround the element under test are within a specified range of one ormore specified thresholds for the graphic components.
 15. The system asrecited in claim 14, wherein the specified distance is the radius usedby the test area-based region growing mechanism.
 16. The system asrecited in claim 11, wherein the image data set is a two-dimensionalimage, wherein the image elements are pixels, and wherein the region isa two-dimensional region within the two-dimensional image.
 17. Thesystem as recited in claim 16, wherein the two-dimensional image is amedical image.
 18. The system as recited in claim 11, wherein the imagedata set is a three-dimensional image set comprising a plurality ofslices, wherein each slice is represented by a two-dimensional image,wherein the image elements are voxels, and wherein the region and theextended region are three-dimensional volumes within thethree-dimensional image set.
 19. The system as recited in claim 18,wherein the three-dimensional image set is a medical image.
 20. Thesystem as recited in claim 18, wherein the three-dimensional image setis captured using one of Magnetic Resonance Imaging (MRI), computedtomography (CT), positron emission tomography (PET), ultrasound, andX-Ray.
 21. A computer-readable storage medium, comprising programinstructions, wherein the program instructions are computer-executableto implement a dilation mechanism configured to: receive a regiongenerated by an image segmentation mechanism configured to generate theregion comprising a subset of image elements within an object ofinterest in an image data set, wherein the image segmentation mechanismleaves an edge inset between the boundary of the region and the trueedge of the object of interest; extend the region generated by the imagesegmentation mechanism to generate an extended region including at leasta portion of image elements in an area defined by the edge inset; andlimit the extension of the region to a specified distance from the outerboundary of the original region generated by the image segmentationmechanism.
 22. The computer-readable storage medium as recited in claim21, wherein, to extend the region generated by the image segmentationmechanism, the dilation mechanism is further configured to: for eachelement in a set of image elements to be tested that includes at leastthe image elements in the area defined by the edge inset: get an elementthat is connected to at least one element currently in the extendedregion, wherein the extended region includes the elements in theoriginal region generated by the image segmentation mechanism and allelements that have been added to the original region; add the element tothe extended region if the element is within the specified distance fromthe boundary of the original region generated by the image segmentationmechanism and if one or more graphic components of the element arewithin a specified range of one or more specified thresholds for thegraphic components.
 23. The computer-readable storage medium as recitedin claim 21, wherein, to extend the region generated by the imagesegmentation mechanism, the dilation mechanism is further configured to:for each element in a set of image elements to be tested that includesat least the image elements in the area defined by the edge inset: applya connectivity test to the element, wherein said connectivity testdetermines if the element is connected to at least one element currentlyin the extended region, wherein the extended region includes theoriginal region generated by the image segmentation mechanism and allelements that have been added to the original region; apply a distancetest to the element if the element passes the connectivity test, whereinsaid distance test determines if the element is within the specifieddistance from the boundary of the original region generated by the imagesegmentation mechanism; apply a threshold test to the element if theelement passes the connectivity test and the distance test, wherein saidthreshold test determines if one or more graphic components of theelement are within a specified range of one or more specified thresholdsfor the graphic components; and add the element to the extended regionif the element passes the threshold test.
 24. The computer-readablestorage medium as recited in claim 21, wherein said image segmentationmechanism is a test area-based region growing mechanism, wherein a testarea is defined by a specified radius, and wherein said test area-basedregion growing mechanism is configured to add elements to a region beinggrown using a threshold test that determines if one or more graphiccomponents of an element under test and of all elements within a testarea defined by the radius around the element under test are within aspecified range of one or more specified thresholds for the graphiccomponents.
 25. The computer-readable storage medium as recited in claim24, wherein the specified distance is the radius used by the testarea-based region growing mechanism.
 26. The computer-readable storagemedium as recited in claim 1, wherein the image data set is atwo-dimensional image, wherein the image elements are pixels, andwherein the region is a two-dimensional region within thetwo-dimensional image.
 27. The computer-readable storage medium asrecited in claim 26, wherein the two-dimensional image is a medicalimage.
 28. The computer-readable storage medium as recited in claim 21,wherein the image data set is a three-dimensional image set comprising aplurality of slices, wherein each slice is represented by atwo-dimensional image, wherein the image elements are voxels, andwherein the region and the extended region are three-dimensional volumeswithin the three-dimensional image set.
 29. The computer-readablestorage medium as recited in claim 28, wherein the three-dimensionalimage set is a medical image.
 30. The computer-readable storage mediumas recited in claim 28, wherein the three-dimensional image set iscaptured using one of Magnetic Resonance Imaging (MRI), computedtomography (CT), positron emission tomography (PET), ultrasound, andX-Ray.